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  <div class="section" id="geosnap-analyze-cluster">
<h1>geosnap.analyze.cluster<a class="headerlink" href="#geosnap-analyze-cluster" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="geosnap.analyze.cluster">
<code class="sig-prename descclassname">geosnap.analyze.</code><code class="sig-name descname">cluster</code><span class="sig-paren">(</span><em class="sig-param">gdf</em>, <em class="sig-param">n_clusters=6</em>, <em class="sig-param">method=None</em>, <em class="sig-param">best_model=False</em>, <em class="sig-param">columns=None</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">time_var='year'</em>, <em class="sig-param">id_var='geoid'</em>, <em class="sig-param">scaler='std'</em>, <em class="sig-param">pooling='fixed'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/geosnap/analyze/analytics.html#cluster"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#geosnap.analyze.cluster" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a geodemographic typology by running a cluster analysis on the study area’s neighborhood attributes.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>gdf</strong><span class="classifier"><a class="reference external" href="https://geopandas.readthedocs.io/en/latest/reference/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.7.0+39.g9b17975)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a>, required</span></dt><dd><p>long-form GeoDataFrame containing neighborhood attributes</p>
</dd>
<dt><strong>n_clusters</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, required</span></dt><dd><p>the number of clusters to model. The default is 6).</p>
</dd>
<dt><strong>method</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> [‘kmeans’, ‘ward’, ‘affinity_propagation’, ‘spectral’,’gaussian_mixture’, ‘hdbscan’], required</span></dt><dd><p>the clustering algorithm used to identify neighborhood types</p>
</dd>
<dt><strong>best_model</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a>, optional</span></dt><dd><p>if using a gaussian mixture model, use BIC to choose the best
n_clusters. (the default is False).</p>
</dd>
<dt><strong>columns</strong><span class="classifier">list-like, required</span></dt><dd><p>subset of columns on which to apply the clustering</p>
</dd>
<dt><strong>verbose</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.8)"><span class="xref std std-ref">bool</span></a>, optional</span></dt><dd><p>whether to print warning messages (the default is False).</p>
</dd>
<dt><strong>time_var</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional</span></dt><dd><p>which column on the dataframe defines time and or sequencing of the
long-form data. Default is “year”</p>
</dd>
<dt><strong>id_var</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, optional</span></dt><dd><p>which column on the long-form dataframe identifies the stable units
over time. In a wide-form dataset, this would be the unique index</p>
</dd>
<dt><strong>scaler</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">scaler</span></code> from <a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing" title="(in scikit-learn v0.22)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code></a>, optional</span></dt><dd><p>a scikit-learn preprocessing class that will be used to rescale the
data. Defaults to sklearn.preprocessing.StandardScaler</p>
</dd>
<dt><strong>pooling</strong><span class="classifier">[“fixed”, “pooled”, “unique”], optional (default=’fixed’)</span></dt><dd><p>How to treat temporal data when applying scaling. Options include:</p>
<ul class="simple">
<li><p>fixed : scaling is fixed to each time period</p></li>
<li><p>pooled : data are pooled across all time periods</p></li>
<li><p>unique : if scaling, apply the scaler to each time period, then generate
clusters unique to each time period.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl>
<dt><strong>gdf</strong><span class="classifier"><a class="reference external" href="https://geopandas.readthedocs.io/en/latest/reference/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.7.0+39.g9b17975)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>GeoDataFrame with a column of neighborhood cluster labels
appended as a new column. If cluster method exists as a column on the DataFrame
then the column will be incremented.</p>
</dd>
<dt><strong>model</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">named</span></code> <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A tuple with attributes X, columns, labels, instance, W, which store the
input matrix, column labels, fitted model instance, and spatial weights matrix</p>
</dd>
<dt><strong>model_name</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.8)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of model to be stored in a Community</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

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